measure-agent-task-success SKILL.md skillA
measure-agent-task-success from contextjet-ai/awesome-llm-observability is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add contextjet-ai/awesome-llm-observability/measure-agent-task-success@git:20260705.95f5ceaPins git:20260705.95f5cea in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- contextjet-ai/awesome-llm-observability
- Path
skills/measure-agent-task-success/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260705.95f5cea, committed Jul 5, 2026
- Size
- 3,271 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
Use this to measure whether an AI agent actually completed its task end to end, not just whether individual LLM calls looked fine. Trigger on "is my agent working", "measure agent success rate", "evaluate my agent", "how good is my agent", "agent completion rate", or evaluating a multi-step/tool-using agent. Score the outcome of the whole task, plus the path it took.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Measure agent task success A chatbot is judged per response; an *agent* is judged on whether it finished the job. An agent can produce great-looking individual steps and still fail the task (wrong final state, gave up, looped, took a destructive action). Measure at the task level. ## Score the outcome first, then the path **Outcome (did it succeed?)** - the primary metric: - **Task success rate:** did the agent reach the correct end state? Define "success" concretely per task type (the ticket was resolved, the code passed tests, the correct record was updated, the right answer with the right action taken). - Prefer a **checkable** success signal where possible (tests pass, DB in expected state, API returned expected result) over a judged one. For open-ended tasks, use an LLM-as-judge against a rubric, calibrated. **Path (how well did it get there?)** - the diagnostic metrics: - **Steps / tool calls to completion** (efficiency; blowups mean loops or confusion). - **Tool-call success rate** (did tools get called with valid args and succeed?). - **Cost + latency per task** (rolled up across all the agent's calls). - **Recovery:** did it recover from a tool error, or fail/loop? …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/measure-agent-task-success@sha256:1f7e84c40383970eThe line at the top of this page pins the label git:20260705.95f5cea, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_scqk3fqfzirgw3ri)
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Versions
The registry has recorded 1 version of this file, committed on Jul 5, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (3271 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
contextjet-ai/awesome-llm-observability · 45 stars · license a license file GitHub does not identify · pushed 2026-11-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_scqk3fqfzirgw3ri GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/measure-agent-task-success GET https://markdownregistry.com/api/v1/blob/1f7e84c40383970e6c594891c7466cae48315b3324d13374bd7d95da5106fc9b
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.